CN109667727B - Wind turbine generator yaw error inherent deviation identification and compensation method based on power curve analysis - Google Patents

Wind turbine generator yaw error inherent deviation identification and compensation method based on power curve analysis Download PDF

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CN109667727B
CN109667727B CN201811372808.7A CN201811372808A CN109667727B CN 109667727 B CN109667727 B CN 109667727B CN 201811372808 A CN201811372808 A CN 201811372808A CN 109667727 B CN109667727 B CN 109667727B
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yaw error
interval
wind speed
wind turbine
turbine generator
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CN109667727A (en
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杨秦敏
鲍雨浓
陈积明
孙优贤
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Zhejiang University ZJU
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/0204—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor for orientation in relation to wind direction
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/04—Automatic control; Regulation
    • F03D7/042—Automatic control; Regulation by means of an electrical or electronic controller
    • F03D7/043—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00—Control
    • F05B2270/30—Control parameters, e.g. input parameters
    • F05B2270/32—Wind speeds
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00—Control
    • F05B2270/30—Control parameters, e.g. input parameters
    • F05B2270/324—Air pressure
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00—Control
    • F05B2270/30—Control parameters, e.g. input parameters
    • F05B2270/325—Air temperature
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00—Control
    • F05B2270/30—Control parameters, e.g. input parameters
    • F05B2270/335—Output power or torque
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/72—Wind turbines with rotation axis in wind direction

Abstract

The invention discloses a wind turbine generator yaw error inherent deviation identification and compensation method based on power curve analysis. The method is based on real-time operation data of a wind turbine generator data acquisition and monitoring control (SCADA) system including wind speed, active power, yaw error, environment temperature, environment air pressure and the like, firstly, the data are preprocessed to a certain degree, then, the wind speed and the power data are divided according to a certain yaw error interval, power curves under different yaw error intervals are respectively fitted through a standard power curve fitting process, further, different power curves are subjected to quantitative analysis, an interval range of yaw error inherent deviation values is determined based on an interval judgment criterion, and finally, the identified inherent deviation values are directly compensated to actual yaw error measurement values in an incremental mode. The method is based on data driving, has no special requirements on the operation data of the wind turbine generator, has strong universality and has strong application value on the performance improvement of the wind turbine generator.

Description

Wind turbine generator yaw error inherent deviation identification and compensation method based on power curve analysis
Technical Field
The invention relates to a method for identifying and compensating the inherent deviation of a yaw error of a wind turbine generator, in particular to a method for identifying and compensating the inherent deviation of the yaw error of the wind turbine generator based on power curve analysis.
Background
In the modern society with the shortage of traditional fossil energy resources and serious pollution, wind energy is widely popular to the public as a new pollution-free and renewable energy, and the wind power industry is one of novel renewable energy industries which are vigorously developed at home and abroad. In China, the construction and related research work of wind power plants are remarkably improved in quantity and quality in the last decade, but a series of negative factors caused by the continuous degradation of wind generating sets are accompanied while the wind power generation industry is vigorously developed. In the use process of the existing wind turbine generator, because the wind speed has the characteristics of intermittency and uncertainty of height, the performance evaluation of the wind turbine generator is greatly influenced, the performance condition of the wind turbine generator is accurately evaluated, and effective means for improving the performance of the wind turbine generator are the important point for improving the competitiveness of wind power in new energy power generation.
At present, when a wind power generation system is used for dealing with wind direction changes, the maximum wind energy capture efficiency is obtained by adjusting a yaw system. Fig. 2 is a schematic view of a yaw control strategy of a wind turbine generator, where a specific control strategy of a yaw system and an actuator is to ensure that a yaw error value is as small as possible, and a physical meaning in practice is to control a swept surface of a blade of the wind turbine generator to face an incoming wind direction as much as possible, that is, to control an angle of a yaw error θ to be as close to 0 ° as possible. In the related application of the current wind power industry, the wind turbine generator adopts a direct measurement mode for determining the yaw error angle: namely, a wind direction indicator is arranged behind the cabin and the position of a zero reticle of the wind direction indicator is calibrated to be parallel to the direction of the cabin; under the normal operation condition of the wind turbine generator, the sensor feeds a measured wind direction value back to the yaw system, and the yaw system controls the cabin to adjust the direction of the incoming wind based on a yaw control strategy of the yaw system. However, the anemoscope mainly has the following two problems during actual installation and operation and maintenance:
(1) the installation worker usually does not need to use measuring equipment, but only uses experience or visual measurement to calibrate the zero reticle position of the anemoscope;
(2) during repeated rotation of the wind vane during actual operation, return errors can also occur for mechanical reasons.
The gradual accumulation of the two aspects tends to bring large errors to the measurement of the yaw error angle, thereby affecting the performance of the yaw system. Therefore, under the background that the related research of the intelligent identification and compensation technology based on data analysis in the field of performance improvement of the wind turbine generator is still in the technical gap, based on the research idea of controller improvement, the inherent deviation value of the yaw error needs to be identified and fed back to the yaw system to compensate for the problem of zero position error of the wind turbine generator cabin anemoscope, so that the purpose of improving the output performance of the wind power generation system is very significant.
Disclosure of Invention
The invention aims to fill the technical blank of the intelligent identification and compensation technology based on data analysis in the field of performance improvement of wind turbines, and provides a method for identifying and compensating the inherent deviation of the yaw error of a wind turbine based on power curve analysis. The method is based on data analysis, real power curves of the wind turbine generator under different yaw error intervals are fitted, corresponding indexes are designed for performance quantification, and finally, the identification and compensation of the yaw error inherent deviation are achieved by combining a simple and effective yaw error inherent deviation identification criterion and a yaw error inherent deviation compensation strategy, so that the method has high practical application value for improving the power generation output performance of the wind turbine generator.
The purpose of the invention is realized by the following technical scheme: a wind turbine generator yaw error inherent deviation identification and compensation method based on power curve analysis comprises the following steps:
1) reading wind turbine generator operation data information measured in an SCADA system of N wind turbine generators to be analyzed in corresponding demand periods according to the wind turbine generator yaw error inherent deviation identification and compensation demands, wherein the information comprises wind speed { v } viActive power { P }i}, ambient air pressure { Bi}, ambient temperature { TiAnd yaw error thetaiRecording the information data set as
Figure BDA0001869807430000021
Wherein i is 1, 2, 3, …, N;
2) based on the information data set in step 1)Calculating to obtain the air density [ rho ] of the corresponding momentiAnd wind speed { v } in the information data setiCorrection of the information to a reference air density ρ0Corrected wind speed of
Figure BDA0001869807430000023
Wherein i is 1, 2, 3, …, N;
3) correcting the wind speed in the step 2)
Figure BDA0001869807430000024
Active power { PiAnd yaw error thetaiSynthesizing into a wind turbine yaw error inherent deviation analysis data set { X }iAnd will { X at a certain yaw error intervaliDivide it into M intervals, note the data number in the k yaw error division interval is NkThe yaw error inherent bias analysis data set is
Figure BDA0001869807430000025
Wherein k is 1, 2, 3, …, M, l is 1, 2, 3, …, Nk；
4) Yaw error inherent deviation analysis data set based on M intervalsRespectively fitting M real power curves, and recording the real power curve under the k-th yaw error interval as { PCkWhere k is 1, 2, 3, …, M;
5) respectively calculating real Power Curves (PC) under M yaw error intervalskRespective quantization performance index PIkWherein k is 1, 2, 3, …, M;
6) determining the yaw error inherent deviation value theta of the wind turbine generator set through the yaw error inherent deviation identification criterionimAnd identifying the deviation value thetaimCompensating to the actual yaw error measurement value theta directly in an incremental manner to obtain a final compensated yaw error true value theta';
the yaw error inherent deviation identification criterion is defined as follows: real Power Curve (PC) under all M yaw error intervals in the step 5)kQuantized Performance indicator PIkArranged from big to small and selects the maximum quantization performance index PImaxCorrespond toThe index k' of the interval, the yaw error intrinsic bias value thetaimThe identification result calculation formula is as follows
Figure BDA0001869807430000027
Wherein theta islbAnd thetaubRespectively a lower bound and an upper bound of the yaw error range to be analyzed.
As a further elaboration, in step 2) of the method, the density ρ of the airiAnd correcting wind speed
Figure BDA0001869807430000031
The calculation formula of (a) is as follows:
2-a) air density ρi：
Figure BDA0001869807430000032
Wherein R is0Is the specific gas constant of the drying air; rwIs the specific gas constant of water vapor; pwIs the pressure of water vapor; b isiFor ambient air pressure, obtained by SCADA system, or by
Figure BDA0001869807430000033
Estimate of where B0The standard sea level atmospheric pressure is adopted, e is a natural constant, g is gravity acceleration, z is the altitude at the hub of the wind turbine generator, and R is a specific gas constant of air;
Figure BDA0001869807430000034
for relative ambient humidity, obtained or set by SCADA system
2-b) correcting the wind speed
Figure BDA0001869807430000036
Figure BDA0001869807430000037
Where ρ is0Is referred to as air density.
As a further elaboration, in step 3) of the method, the yaw error intrinsic bias analysis dataset { XiThe method for dividing the intervals comprises the following steps:
3-a) plotting yaw error θiThe frequency distribution histogram of the yaw error range to be analyzed is set, and the lower limit theta of the yaw error range to be analyzed is set based on the distribution condition of the frequency distribution histogramlbAnd an upper bound θub；
3-b) set yaw error intrinsic bias analysis dataset { XiDividing the interval of the M points into M number;
3-c) of
Figure BDA0001869807430000038
Partitioning intervals for yaw error intervals, analyzing data set { X) for yaw error inherent biasiDivide.
As a further description, in step 4), the flow of acquiring the real power curve of the wind turbine generator under the M yaw error intervals is as follows:
4-a) setting a real power curve to obtain an initial interval k equal to 1;
4-b) determining yaw error intrinsic bias analysis data set under k yaw error intervalCorrected wind speed inCorresponding maximum value
Figure BDA00018698074300000311
Note the book
Figure BDA00018698074300000312
Wherein v iscut_offCutting out wind speed for the wind turbine generator;
4-c) interval at fixed wind speedΔ v is a wind speed interval division interval, and a yaw error inherent deviation analysis data set under the k-th yaw error interval
Figure BDA00018698074300000313
Further based on corrected wind speed
Figure BDA00018698074300000314
Dividing the data into a yaw error inherent deviation analysis data set in the jth corrected wind speed interval
Figure BDA00018698074300000315
Is defined as
Figure BDA00018698074300000316
k＝1，2，3，…，M j＝1，2，3，…，Mkm＝1，2，3，…，Mk，j
Wherein M isk，jAnalyzing the data set for the yaw error inherent deviation in the jth corrected wind speed interval
Figure BDA00018698074300000317
The number of data in (1); mkAnalyzing the data set for the yaw error inherent deviation under the k yaw error interval
Figure BDA0001869807430000041
The number of the corrected wind speed interval divisions is calculated as follows
Figure BDA0001869807430000042
Wherein
Figure BDA00018698074300000427
The function is an upward rounding function;
4-d) calculating the yaw error inherent bias analysis dataset for each corrected wind speed interval
Figure BDA0001869807430000043
Average corrected wind speed of
Figure BDA0001869807430000044
And average active power
Figure BDA0001869807430000045
The formula is as follows
Figure BDA0001869807430000046
4-e) average corrected wind speed for each corrected wind speed interval
Figure BDA0001869807430000047
And average active power
Figure BDA0001869807430000048
Carrying out maximum value-minimum value normalization processing to obtain normalized average corrected wind speedAnd average active power
Figure BDA00018698074300000410
4-f) correcting the wind speed based on the average
Figure BDA00018698074300000411
And average active power
Figure BDA00018698074300000412
Determining the power curve fitting central point under each corrected wind speed interval
Figure BDA00018698074300000413
The determination is as follows: analyzing the data set of the yaw error inherent deviation in the jth corrected wind speed interval
Figure BDA00018698074300000414
Number of data in (M)k，jIf the power curve is equal to 0, the power curve is not fitted to the central point in the interval; otherwise, the power curve fitting center point in the interval is considered
Figure BDA00018698074300000415
4-g) supplementary definition of center point
Figure BDA00018698074300000416
And recording the number of the fitting center points of the power curve in the kth yaw error interval as M'kCalculating the fitted center point of each power curve
Figure BDA00018698074300000417
Corresponding parameter value
Figure BDA00018698074300000418
Is given by the formula
WhereinFitting center points for two adjacent power curves
Figure BDA00018698074300000420
And
Figure BDA00018698074300000421
corresponding to the chord length after coordinate normalization, i.e.
Figure BDA00018698074300000422
dkFitting all power curves with the total chord length normalized by the coordinates corresponding to the center point, i.e.
Figure BDA00018698074300000423
4-h) fitting the power curve in the kth yaw error interval by adopting a least square B spline fitting algorithm, wherein a fitting function B is obtainedk(t) is defined as follows:
Figure BDA00018698074300000424
wherein N isn，p(t) is a standard function of the nth segment of B spline fitting function with the order p, t is an independent variable of the least square B spline fitting function,
Figure BDA00018698074300000425
fitting the nth control point of the function to the least squares B-spline;
Figure BDA00018698074300000426
for a segment node, i ═ 0, 1, 2, …, p-1, p, p +1, …, M'k-1，M′k，M′k+1，…，M′k+ p, the calculation is as follows:
Figure BDA0001869807430000051
4-i) determining a B-spline fitting function B based on a least squares optimization functionkAll control points in (t)
Figure BDA0001869807430000052
Figure BDA0001869807430000053
4-j) solving the least square B-spline fitting function Bk(t) as a result of the true power curve in the kth yaw error interval { PC) converted to a polynomial form with the independent variable as the wind speed vk}；
4-k) setting an interval k to be analyzed as k +1, and repeating the steps 4-b) to 4-j) until j is larger than M.
As a further description, the method stepsIn step 5), the performance index PI is quantifiedkIs defined as follows:
Figure BDA0001869807430000054
wherein N ishIs a number of 1 year time to hour; CAP is rated power of the wind turbine generator to be analyzed;
Figure BDA0001869807430000055
is the median value of the wind speed in the jth corrected wind speed interval under the kth yaw error interval, namely
Figure BDA0001869807430000056
Figure BDA0001869807430000057
True power curve for k-th yaw error interval PCkOn (c)
Figure BDA0001869807430000058
Corresponding active power value, and
Figure BDA0001869807430000059
the F (-) function is a cumulative probability distribution function of Rayleigh distribution, and the specific formula is as follows
Figure BDA00018698074300000510
Wherein v isaveThe annual average wind speed of the wind turbine generator to be analyzed.
Compared with the prior art, the invention has the following innovative advantages and remarkable effects:
1) the method for identifying and compensating the yaw error inherent deviation of the wind turbine generator based on the power curve analysis is innovatively provided, and the technical blank of the intelligent identification and compensation technology based on the data analysis in the field of performance improvement of the wind turbine generator is filled.
2) The method designs a wind turbine performance improving flow including the steps of wind turbine real power curve fitting, power curve energy index construction, yaw error inherent deviation identification, yaw error inherent deviation compensation strategy formulation and the like, and has strong practicability, reliability and expansibility.
Drawings
FIG. 1 is a flow chart of a method for identifying and compensating the inherent deviation of the yaw error of the wind turbine generator based on power curve analysis according to the invention;
FIG. 2 is a schematic diagram illustrating an angular correlation relationship such as an inherent yaw error deviation of a wind turbine generator in the field of application of the present invention;
FIG. 3 is a scatter diagram of raw data of a power curve of a wind turbine in step 1) according to an embodiment of the present invention;
FIG. 4 is a histogram of yaw error frequency distribution before compensation in step 3) when the present invention is applied to an embodiment;
FIG. 5 is a graph showing the results of correlation of power curves of the yaw error interval [ -1 °, 0 ° ] in steps 3) and 4) when the present invention is applied to the examples;
FIG. 6 is a graph showing the result of the power curve quantization performance index in each yaw error interval before the yaw error inherent offset compensation in step 5) when the present invention is applied to the embodiment;
fig. 7 is a diagram showing a result of a power curve quantization performance index in each yaw error interval after the yaw error inherent offset compensation in step 6) when the present invention is applied to the embodiment.
Detailed Description
The following detailed description of the embodiments and the working principles of the present invention is made with reference to the accompanying drawings:
examples
Because the wind conditions of the wind turbines in the wind power plant are difficult to be completely consistent when the wind turbines operate in different time periods, for the verification of the effectiveness of the method, the data adopted in the embodiment is simulation data of GH blade 3.82 under the same type of wind turbine and the same wind file to analyze and research the inherent deviation identification and compensation method of the yaw error of the wind turbine. The data sampling interval of the wind turbine generator is 10min, the data information is 5 years in period, and 284405 pieces are in total. The specific variables and related data information included in the data set are shown in tables 1 and 2:
simulation data set variable information of wind turbine generator of certain model under Table 1 GH Bladed 3.82
Variable names Meaning of variables Variable unit
Wind speed v Current wind turbine generator system cabin wind speed m/s
Active power P Active power of current wind turbine generator kW
Ambient temperature T Operating environment temperature of wind turbine generator ℃
Ambient air pressure B Wind turbine generator system operating environment air pressure Pa
Yaw error theta Yaw error of current wind turbine generator °
Part simulation data of certain type of wind turbine generator under certain wind file under Table 2 GH Bladed 3.82
Data sequence number Wind speed Active power Ambient temperature Ambient air pressure Yaw error
… … … … … …
105679 4.2992 81.0290 25.0000 100463.2887 5.7852
105680 4.5417 81.8810 25.0000 100463.2887 15.2980
105681 4.9667 82.8700 25.0000 100463.2887 1.6641
… … … … … …
235640 11.6990 1504.7000 25.0000 100463.2887 6.0619
235641 11.5200 1549.5000 25.0000 100463.2887 9.1317
235642 11.1470 1550.0000 25.0000 100463.2887 -0.0520
… … … … … …
It is worth mentioning that the yaw error measured in the GH Bladed does not have the inherent deviation of the yaw error existing in the measurement process of the anemoscope in the actual application process, so that the phenomenon that the inherent deviation of the yaw error of minus 5 degrees exists in the actual process is simulated by adopting a mode of artificially changing the measured value to plus 5 degrees in the simulation process. In this embodiment, the implementation of the yaw error inherent deviation identification and compensation method is performed by using all the simulation data by default, the method result is the obtained identification result of the yaw error inherent deviation of the wind turbine generator system, and the verification of the method effectiveness is performed by a compensation means, and the detailed implementation steps are as follows:
1) reading wind turbine generator operation data information measured in an SCADA system of N wind turbine generators to be analyzed in corresponding demand periods according to the wind turbine generator yaw error inherent deviation identification and compensation demands, wherein the information comprises wind speed { v } viActive power { P }i}, ambient air pressure { Bi}, ambient temperature { TiAnd yaw error thetaiRecording the information data set as
Figure BDA0001869807430000071
Wherein i is 1, 2, 3, …, N; according to the description of the variable information of the data set listed in tables 1 and 2, the data set in this embodiment includes all necessary information in this step, and the result shown in fig. 3 is a raw data scatter diagram of the power curve of the wind turbine generator system in this step;
2) based on the information data set in step 1)Calculating to obtain the air density [ rho ] of the corresponding momenti} andwind speed { v) in a data set of informationiCorrection of the information to a reference air density ρ0Corrected wind speed of
Figure BDA0001869807430000073
Wherein i is 1, 2, 3, …, N; the correlation calculation formula is as follows:
2-a) air density ρi：
Figure BDA0001869807430000074
Wherein R is0Is the specific gas constant of the drying air; rwIs the specific gas constant of water vapor; pwIs the pressure of water vapor; b isiFor ambient air pressure, obtained by SCADA system, or byEstimate of where B0The standard sea level atmospheric pressure is adopted, e is a natural constant, g is gravity acceleration, z is the altitude at the hub of the wind turbine generator, and R is a specific gas constant of air;for relative ambient humidity, obtained or set by SCADA system
Figure BDA0001869807430000077
In this example, the ambient temperature and the ambient air pressure were fixed at 25 ℃ and 100463.2887Pa, respectively, so that the air density calculation result was 1.1738kg/m3。
2-b) correcting the wind speed
Figure BDA0001869807430000078
Figure BDA0001869807430000079
Where ρ is0For reference to the air density, 1.225kg/m is taken in this example3。
3) Correcting the wind speed in the step 2)
Figure BDA00018698074300000710
Active power { PiAnd yaw error thetaiSynthesizing into a wind turbine yaw error inherent deviation analysis data set { X }iAnd will { X at a certain yaw error intervaliDivide it into M intervals, note the data number in the k yaw error division interval is NkThe yaw error inherent bias analysis data set isWherein k is 1, 2, 3, …, M, l is 1, 2, 3, …, Nk(ii) a One preferred method of using the yaw error interval division is as follows, but is not limited thereto:
3-a) plotting yaw error θiThe frequency distribution histogram of the yaw error range to be analyzed is set, and the lower limit theta of the yaw error range to be analyzed is set based on the distribution condition of the frequency distribution histogramlbAnd an upper bound θub；
3-b) set yaw error intrinsic bias analysis dataset { XiDividing the interval of the M points into M number;
3-c) of
Figure BDA0001869807430000081
Partitioning intervals for yaw error intervals, analyzing data set { X) for yaw error inherent biasiDividing, and analyzing the yaw error inherent deviation analysis data set of the k-th yaw error intervalIs defined as
Figure BDA0001869807430000083
k＝1，2，3，…，M l＝1，2，3，…，Nk
Wherein N iskAnalyzing the data set for the yaw error inherent deviation under the k yaw error interval
Figure BDA0001869807430000084
The number of data in (1). In the present embodiment, only the corresponding yaw error interval is given as [ -1 °, 0 ° ] for space limitation reasons]The power curve scatter plot of (a) is shown in fig. 5.
4) Yaw error inherent deviation analysis data set based on M intervals
Figure BDA0001869807430000085
Respectively fitting M real power curves, and recording the real power curve under the k-th yaw error interval as { PCkWhere k is 1, 2, 3, …, M; one preferred algorithm flow for the true power curve acquisition employed is as follows, but is not limited thereto:
4-a) setting a real power curve to obtain an initial interval k equal to 1;
4-b) determining yaw error intrinsic bias analysis data set under k yaw error interval
Figure BDA0001869807430000086
Corrected wind speed in
Figure BDA0001869807430000087
Corresponding maximum valueNote the book
Figure BDA0001869807430000089
Wherein v iscut_offCutting out wind speed for the wind turbine generator;
4-c) dividing intervals by taking a fixed wind speed interval delta v as a wind speed interval, and analyzing a data set of yaw error inherent deviation under the k-th yaw error interval
Figure BDA00018698074300000810
Further based on corrected wind speed
Figure BDA00018698074300000811
Dividing into j th correction windYaw error inherent bias analysis dataset at speed interval
Figure BDA00018698074300000812
Is defined as
k＝1，2，3，…，M j＝1，2，3，…，Mkm＝1，2，3，…，Mk，j
Wherein M isk，jAnalyzing the data set for the yaw error inherent deviation in the jth corrected wind speed intervalThe number of data in (1); mkAnalyzing the data set for the yaw error inherent deviation under the k yaw error interval
Figure BDA00018698074300000815
The number of the corrected wind speed interval divisions is calculated as follows
Figure BDA00018698074300000816
Wherein
Figure BDA00018698074300000821
The function is an upward rounding function;
4-d) calculating the yaw error inherent bias analysis dataset for each corrected wind speed interval
Figure BDA00018698074300000817
Average corrected wind speed of
Figure BDA00018698074300000818
And average active powerThe formula is as follows
Figure BDA00018698074300000820
4-e) average corrected wind speed for each corrected wind speed interval
Figure BDA0001869807430000091
And average active power
Figure BDA0001869807430000092
Carrying out maximum value-minimum value normalization processing to obtain normalized average corrected wind speed
Figure BDA0001869807430000093
And average active power
4-f) correcting the wind speed based on the average
Figure BDA0001869807430000095
And average active power
Figure BDA0001869807430000096
Determining the power curve fitting central point under each corrected wind speed interval
Figure BDA0001869807430000097
The determination is as follows: analyzing the data set of the yaw error inherent deviation in the jth corrected wind speed interval
Figure BDA0001869807430000098
Number of data in (M)k，jIf the power curve is equal to 0, the power curve is not fitted to the central point in the interval; otherwise, the power curve fitting center point in the interval is considered
Figure BDA0001869807430000099
4-g) supplementary definition of center pointAnd recording the number of the fitting center points of the power curve in the kth yaw error interval as M'kCalculating the fitted center point of each power curve
Figure BDA00018698074300000911
Corresponding parameter valueIs given by the formula
Figure BDA00018698074300000913
Wherein
Figure BDA00018698074300000914
Fitting center points for two adjacent power curves
Figure BDA00018698074300000915
And
Figure BDA00018698074300000916
corresponding to the chord length after coordinate normalization, i.e.
Figure BDA00018698074300000917
dkFitting all power curves with the total chord length normalized by the coordinates corresponding to the center point, i.e.
Figure BDA00018698074300000918
4-h) fitting the power curve in the kth yaw error interval by adopting a least square B spline fitting algorithm, wherein a fitting function B is obtainedk(t) is defined as follows:
Figure BDA00018698074300000919
wherein N isn，p(t) is the nth segment of B spline fitting function with the order of pT is the argument of the least squares B-spline fitting function,
Figure BDA00018698074300000920
fitting the nth control point of the function to the least squares B-spline;
Figure BDA00018698074300000921
for a segment node, i ═ 0, 1, 2, …, p-1, p, p +1, …, M'k-1，M′k，M′k+1，…，M′k+ p, the calculation is as follows:
Figure BDA00018698074300000922
4-i) determining a B-spline fitting function B based on a least squares optimization functionkAll control points in (t)
Figure BDA00018698074300000923
Figure BDA00018698074300000924
4-j) solving the least square B-spline fitting function Bk(t) as a result of the true power curve in the kth yaw error interval { PC) converted to a polynomial form with the independent variable as the wind speed vk}；
4-k) setting the interval k to be analyzed as k +1, and repeating the steps 4-b) to 4J) until J > M. Due to space limitations, the calculation process and secondary results of each process parameter are omitted in the fitting of the relevant power curve in the embodiment, and the relevant important parameters take the following values: corrected wind speed corresponds to a maximum value of
Figure BDA0001869807430000101
The fixed wind speed interval delta v is 2m/s, and the 14 th yaw error interval is [ -1 DEG, 0 DEG ]]The number of the corrected wind speed intervals in the power curve related data is 15, and the fitting center point and the fitting result of the corresponding real power curve are shown in the figureThe symbol "■" in FIG. 5 and the curve.
5) Respectively calculating real Power Curves (PC) under M yaw error intervalskRespective quantization performance index PIkWherein k is 1, 2, 3, …, M; quantitative performance index PIkIs defined as follows:
Figure BDA0001869807430000102
wherein N ishIs a number of 1 year time to hour; CAP is rated power of the wind turbine generator to be analyzed;is the median value of the wind speed in the jth corrected wind speed interval under the kth yaw error interval, namelyAnd is
Figure BDA0001869807430000105
True power curve for k-th yaw error interval PCkOn (c)
Figure BDA0001869807430000107
Corresponding active power value, and
Figure BDA0001869807430000108
the F (-) function is a cumulative probability distribution function of Rayleigh distribution, and the specific formula is as follows
Figure BDA0001869807430000109
Wherein v isaveThe annual average wind speed of the wind turbine generator to be analyzed. In this embodiment, the relevant important parameters take the following values: n is a radical ofh8760 is calculated according to 365 days in 1 year; CAP is rated power value of the wind turbine generator set, and 1 is taken550kW；vaveTaking the average wind speed of the simulation wind file as 7m/s, corresponding to the respective quantitative performance indexes PI of the real power curves under 20 yaw error intervalskThe calculation results are shown in fig. 6.
6) Determining the yaw error inherent deviation value theta of the wind turbine generator set through the yaw error inherent deviation identification criterionimAnd identifying the deviation value thetaimCompensating the actual measured value theta of the yaw error in an incremental mode directly to obtain the final compensated real value theta' of the yaw error, namely theta ═ theta + thetaim；
The yaw error inherent deviation identification criterion is defined as follows: real Power Curve (PC) under all M yaw error intervals in the step 5)kQuantized Performance indicator PIkArranged from big to small and selects the maximum quantization performance index PImaxCorresponding to the index k' of the interval, the inherent deviation value theta of the yaw errorimThe identification result calculation formula is as follows
Figure BDA00018698074300001010
Wherein theta islbAnd thetaubRespectively a lower bound and an upper bound of the yaw error range to be analyzed. In this embodiment, the quantization performance index PI of the real power curve in fig. 6kMaximum value of (PI)maxMarked by using the ★ symbol, the serial number of the interval corresponding to the maximum value is 9, i.e. the yaw error interval is [ -6 °, -5 ° ]]Then the yaw error intrinsic bias value theta can be usedimCalculating the identification result of the inherent deviation value of the yaw error to be-5.5 degrees by using the identification result calculation formula; further based on a yaw error inherent deviation compensation strategy, the measured value theta of the yaw error is artificially added by 5.5 degrees to be changed into corrected theta ', namely theta' is equal to theta +5.5 degrees; the corrected result is used as yaw control input to perform data simulation after the wind turbine generator is compensated again under the same wind file, and the respective quantitative performance indexes PI of the real power curves shown in the figure 7 can be obtained through the same analysis processkThe calculation results show that the relevant important parameters are as follows: frequency division of yaw errorThe 10% and 90% quantiles of the cloth histogram are-9.681 ° and 10.498 °, respectively, i.e., the lower bound of yaw error θlbAnd an upper bound θubRespectively at-10 ° and 10 °; taking 20 as the number M of interval division; corrected wind speed corresponds to a maximum value of
Figure BDA0001869807430000111
The fixed wind speed interval delta v is 2m/s, and other key parameters are the same as the parameters before compensation. As can be seen from fig. 7, after the identification and compensation of the inherent deviation of the yaw error, the serial number of the section corresponding to the maximum value is 10, that is, the identification section of the inherent deviation of the yaw error after the compensation is [0 °, 1 ° ]]Therefore, the compensation of the inherent deviation improves the yaw control effect under the existence of the inherent deviation, and the result value of the power curve quantization index can also show that the power curve quantization index result under the same yaw position is also improved by 20-30 h, and compared with the performance before the compensation, the performance is improved by about 0.8-1.2%. Therefore, the effectiveness and the practicability of the wind turbine generator yaw error inherent deviation identification and compensation method based on power curve analysis are successfully verified on a simulation data set of GH Bladed 3.82 simulation software.
The invention relates to a method for identifying and compensating the inherent deviation of a yaw error of a wind turbine generator based on power curve analysis, which mainly comprises links such as wind speed correction based on air density, division of a yaw error interval, real power curve fitting of the wind turbine generator, power curve quantitative index calculation, identification and compensation of the inherent deviation of the yaw error and the like. FIG. 1 is a specific flow of real-time and application of a wind turbine yaw error inherent deviation identification and compensation method based on power curve analysis. According to the whole embodiment, analysis is carried out based on SCADA data of the wind turbine generator according to the process shown in FIG. 1, and the performance improvement requirement of the wind turbine generator is realized by fitting the real power curve of the wind turbine generator under different yaw error intervals and finally based on the identification criterion and the compensation strategy of the inherent deviation of the yaw error. Fig. 2 to 7 show results of each link in the flow of identifying and compensating the yaw error inherent deviation of the wind turbine generator by using the method for identifying and compensating the yaw error inherent deviation of the wind turbine generator based on power curve analysis according to the present invention, which has strong application value and significance for enterprises with requirements for performance improvement of the wind turbine generator.

Claims (4)

1. A wind turbine generator yaw error inherent deviation identification and compensation method based on power curve analysis is characterized by comprising the following steps:
1) reading wind turbine generator operation data information measured in an SCADA system of N wind turbine generators to be analyzed in corresponding demand periods according to the wind turbine generator yaw error inherent deviation identification and compensation demands, wherein the information comprises wind speed { v } viActive power { P }i}, ambient air pressure { Bi}, ambient temperature { TiAnd yaw error thetaiRecording the information data set as
Figure FDA0002181362190000011
Wherein i is 1, 2, 3, …, N;
2) based on the information data set in step 1)
Figure FDA0002181362190000012
Calculating to obtain the air density [ rho ] of the corresponding momentiAnd wind speed { v } in the information data setiCorrection of the information to a reference air density ρ0Corrected wind speed of
Figure FDA0002181362190000013
Wherein i is 1, 2, 3, …, N;
3) correcting the wind speed in the step 2)
Figure FDA0002181362190000014
Active power { PiAnd yaw error thetaiSynthesizing into a wind turbine yaw error inherent deviation analysis data set { X }iAnd will { X at a certain yaw error intervaliDivide it into M intervals, note the data number in the k yaw error division interval is NkThe yaw error inherent bias analysis data set is
Figure FDA0002181362190000015
Wherein k is 1, 2, 3, …, M, l is 1, 2, 3, …, Nk；
4) Yaw error inherent deviation analysis data set based on M intervals
Figure FDA0002181362190000016
Respectively fitting M real power curves, and recording the real power curve under the k-th yaw error interval as { PCkWhere k is 1, 2, 3, …, M;
5) respectively calculating real Power Curves (PC) under M yaw error intervalskRespective quantization performance index PIkWherein k is 1, 2, 3, …, M; quantitative performance index PIkIs defined as follows:
Figure FDA0002181362190000017
wherein N ishIs a number of 1 year time to hour; CAP is rated power of the wind turbine generator to be analyzed;
Figure FDA0002181362190000018
is the median value of the wind speed in the jth corrected wind speed interval under the kth yaw error interval, namely
Figure FDA0002181362190000019
And is
Figure FDA00021813621900000110
Figure FDA00021813621900000111
True power curve for k-th yaw error interval PCkOn (c)Corresponding active power value, and
Figure FDA00021813621900000113
the F (-) function is a cumulative probability distribution function of Rayleigh distribution, and the specific formula is as follows
Figure FDA00021813621900000114
Wherein v isaveThe annual average wind speed of the wind turbine generator to be analyzed;
6) determining the yaw error inherent deviation value theta of the wind turbine generator set through the yaw error inherent deviation identification criterionimAnd identifying the deviation value thetaimCompensating to the actual yaw error measurement value theta directly in an incremental manner to obtain a final compensated yaw error true value theta';
the yaw error inherent deviation identification criterion is defined as follows: real Power Curve (PC) under all M yaw error intervals in the step 5)kQuantized Performance indicator PIkArranged from big to small and selects the maximum quantization performance index PImaxCorresponding to the index k' of the interval, the inherent deviation value theta of the yaw errorimThe identification result calculation formula is as follows
Figure FDA0002181362190000021
Wherein theta islbAnd thetaubRespectively a lower bound and an upper bound of the yaw error range to be analyzed.
2. The method for identifying and compensating the yaw error inherent deviation of the wind turbine generator based on the power curve analysis as claimed in claim 1, wherein in the step 2), the air density p isiAnd correcting wind speedThe calculation formula of (a) is as follows:
2-a) air density ρi：
Figure FDA0002181362190000023
Wherein R is0Is the specific gas constant of the drying air; rwIs the specific gas constant of water vapor; pwIs the pressure of water vapor; b isiFor ambient air pressure, obtained by SCADA system, or by
Figure FDA0002181362190000024
Estimate of where B0The standard sea level atmospheric pressure is adopted, e is a natural constant, g is gravity acceleration, z is the altitude at the hub of the wind turbine generator, and R is a specific gas constant of air;for relative ambient humidity, obtained or set by SCADA system
Figure FDA0002181362190000026
2-b) correcting the wind speed
Figure FDA0002181362190000027
Figure FDA0002181362190000028
Where ρ is0Is referred to as air density.
3. The method for identifying and compensating the yaw error inherent deviation of the wind turbine generator set based on the power curve analysis as claimed in claim 1, wherein in the step 3), the yaw error inherent deviation analysis data set { X }iThe interval division method comprises the following steps:
3-a) plotting yaw error θiThe frequency distribution histogram of the yaw error range to be analyzed is set, and the lower limit theta of the yaw error range to be analyzed is set based on the distribution condition of the frequency distribution histogramlbAnd an upper bound θub；
3-b) set yaw error intrinsic bias analysis dataset { XiDividing the interval of the M points into M number;
3-c) of
Figure FDA0002181362190000029
Partitioning intervals for yaw error intervals, analyzing data set { X) for yaw error inherent biasiDivide.
4. The method for identifying and compensating the yaw error inherent deviation of the wind turbine generator based on the power curve analysis as claimed in claim 1, wherein in the step 4), the flow of obtaining the true power curve of the wind turbine generator under M yaw error intervals is as follows:
4-a) setting a real power curve to obtain an initial interval k equal to 1;
4-b) determining yaw error intrinsic bias analysis data set under k yaw error intervalCorrected wind speed in
Figure FDA00021813621900000211
Corresponding maximum value
Figure FDA00021813621900000212
Note the book
Figure FDA00021813621900000213
Wherein v iscut_offCutting out wind speed for the wind turbine generator;
4-c) dividing intervals by taking a fixed wind speed interval delta v as a wind speed interval, and analyzing a data set of yaw error inherent deviation under the k-th yaw error interval
Figure FDA0002181362190000031
Further based on corrected wind speed
Figure FDA0002181362190000032
Dividing the data into a yaw error inherent deviation analysis data set in the jth corrected wind speed interval
Figure FDA0002181362190000033
Is defined as
Figure FDA0002181362190000034
k＝1，2，3，…，M j＝1，2，3，…，Mkm＝1，2，3，…，Mk，j
Wherein M isk，jAnalyzing the data set for the yaw error inherent deviation in the jth corrected wind speed interval
Figure FDA0002181362190000035
The number of data in (1); mkAnalyzing the data set for the yaw error inherent deviation under the k yaw error interval
Figure FDA0002181362190000036
The number of the corrected wind speed interval divisions is calculated as follows
Wherein
Figure FDA0002181362190000038
The function is an upward rounding function;
4-d) calculating the yaw error inherent bias analysis dataset for each corrected wind speed interval
Figure FDA0002181362190000039
Average corrected wind speed of
Figure FDA00021813621900000310
And average active power
Figure FDA00021813621900000311
The formula is as follows
Figure FDA00021813621900000312
4-e) average corrected wind speed for each corrected wind speed intervalAnd average active powerCarrying out maximum value-minimum value normalization processing to obtain normalized average corrected wind speed
Figure FDA00021813621900000315
And average active power
Figure FDA00021813621900000316
4-f) correcting the wind speed based on the average
Figure FDA00021813621900000317
And average active power
Figure FDA00021813621900000318
Determining the power curve fitting central point under each corrected wind speed intervalThe determination is as follows: analyzing the data set of the yaw error inherent deviation in the jth corrected wind speed interval
Figure FDA00021813621900000320
Number of data in (M)k，jIf the power curve is equal to 0, the power curve is not fitted to the central point in the interval; inverse directionThen, the power curve in the interval is considered as the fitting center point
4-g) supplementary definition of center pointAnd recording the number of the fitting center points of the power curve in the kth yaw error interval as M'kCalculating the fitted center point of each power curveCorresponding parameter value
Figure FDA00021813621900000324
Is given by the formula
Figure FDA00021813621900000325
Wherein
Figure FDA00021813621900000326
Fitting center points for two adjacent power curvesAnd
Figure FDA00021813621900000328
corresponding to the chord length after coordinate normalization, i.e.
Figure FDA00021813621900000329
dkFitting all power curves with the total chord length normalized by the coordinates corresponding to the center point, i.e.
Figure FDA00021813621900000330
4-h) fitting the power curve in the kth yaw error interval by adopting a least square B spline fitting algorithm, wherein a fitting function B is obtainedk(t) is defined as follows:
Figure FDA0002181362190000041
wherein N isn，p(t) is a standard function of the nth segment of B spline fitting function with the order p, t is an independent variable of the least square B spline fitting function,
Figure FDA0002181362190000042
fitting the nth control point of the function to the least squares B-spline;for a segment node, i ═ 0, 1, 2, …, p-1, p, p +1, …, M'k-1，M′k，M′k+1，…，M′k+ p, the calculation is as follows:
Figure FDA0002181362190000044
4-i) determining a B-spline fitting function B based on a least squares optimization functionkAll control points in (t)
Figure FDA0002181362190000045
Figure FDA0002181362190000046
4-j) solving the least square B-spline fitting function Bk(t) as a result of the true power curve in the kth yaw error interval { PC) converted to a polynomial form with the independent variable as the wind speed vk}；
4-k) setting an interval k to be analyzed as k +1, and repeating the steps 4-b) to 4-j) until j is larger than M.
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